Transcriptomes of human PBMCs and monocytes in sepsis patients
RNA-seq (gene expression) · human · 460 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 460 samples: 460 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Sepsis pathogenesis and outcome are shaped by the balance between the transcriptional states of systemic inflammation and antimicrobial responseCell reports. Medicine · 2024
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE205672_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA847153. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from the first 400 of 460 sample titles: 400 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM6216611 760086_PBMC_unstim_HC
- GSM6216612 760089_PBMC_unstim_HC
- GSM6216613 760091_PBMC_unstim_HC
- GSM6216614 760093_PBMC_unstim_HC
- GSM6216615 760094_PBMC_unstim_HC
- GSM6216616 760095_PBMC_unstim_HC
- GSM6216617 760096_PBMC_unstim_HC
- GSM6216618 760121_PBMC_unstim_HC
- GSM6216619 760122_PBMC_unstim_HC
- GSM6216620 760193_PBMC_unstim_HC
- GSM6216621 760195_PBMC_unstim_HC
- GSM6216622 760196_PBMC_unstim_HC
- GSM6216623 760197_PBMC_unstim_HC
- GSM6216624 760198_PBMC_unstim_HC
- GSM6216625 760199_PBMC_unstim_HC
- GSM6216626 760200_PBMC_unstim_HC
- GSM6216627 760202_PBMC_unstim_HC
- GSM6216628 760203_PBMC_unstim_HC
- GSM6216629 760205_PBMC_unstim_HC
- GSM6216630 760206_PBMC_unstim_HC
- GSM6216631 760207_PBMC_unstim_HC
- GSM6216632 760208_PBMC_unstim_HC
- GSM6216633 760211_PBMC_unstim_HC
- GSM6216634 760213_PBMC_unstim_HC
- GSM6216635 760214_PBMC_unstim_HC
- GSM6216636 760215_PBMC_unstim_HC
- GSM6216637 760216_PBMC_unstim_HC
- GSM6216638 760217_PBMC_unstim_HC
- GSM6216639 760218_PBMC_unstim_HC
- GSM6216640 760221_PBMC_unstim_HC
- GSM6216641 760222_PBMC_unstim_HC
- GSM6216642 760223_PBMC_unstim_HC
- GSM6216643 760226_PBMC_unstim_HC
- GSM6216644 760227_PBMC_unstim_HC
- GSM6216645 760229_PBMC_unstim_HC
- GSM6216646 760230_PBMC_unstim_HC
- GSM6216647 760231_PBMC_unstim_HC
- GSM6216648 760232_PBMC_unstim_HC
- GSM6216649 760233_PBMC_unstim_HC
- GSM6216650 760235_PBMC_unstim_HC
+ 420 more — browse all 460 samples with per-sample file links →
- GSE294985 Transcriptomes of human whole blood cells in sepsis patients 40 samples shares transcriptomes, sepsis
- GSE279719 Human CD14+ monocytes from early inflammatory arthritis patients before and after a 5day-and 30day-period of methotrexate treatment 72 samples shares monocytes
- GSE285037 Faecalibacterium prausnitzii induces an anti-inflammatory response and a metabolic reprogramming in human monocytes 72 samples shares monocytes
- GSE290679 Metabolic Adaptations Rewire CD4 T Cells in a Subset-Specific Manner in Human Critical Illness with and without Sepsis 47 samples shares sepsis
- GSE279452 Multiomics Analyses Unveil Immune Landscape in Pediatric and Adult Sepsis [scRNA_seq] 266 samples shares sepsis
- GSE311629 Biomimetic model of human small airways reveals functional impacts of the cystic fibrosis microenvironment on monocytes and macrophages 35 samples shares monocytes
- GSE308014 RNA‑seq of TREM1 pathway activation in human monocytes and neutrophils 32 samples shares monocytes
- GSE295173 Multi-modal single-cell sequencing reveals network transition in circulating monocytes that aligns with faster recovery in trauma patients and favors a response to M-CSF [scRNA-Seq] 30 samples shares monocytes
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.